State Representation Learning involves learning low dimensional latent representations from the high dimensional states an agent observes. The representation captures the variation in the environment based on the agent’s actions. This representation helps overcome the curse of dimensionality and improves speed and performance of policy learning algorithms such as RL. Our method of learning a latent state is similar to others where a Convolutional Autoencoder is used to learn a compressed latent representation from pixels observed by an agent [1]}, [2]}, [3]}.
